3K Method: Time-optimal Path Planning for Field Robot

1. Alazzam H, AbuAlghanam O, Sharieh A. Best path in mountain environment based on parallel A* algorithm and apache spark. The Journal of Supercomputing. 2022; 1–20. AlazzamH AbuAlghanamO ShariehA Best path in mountain environment based on parallel A* algorithm and apache spark The Journal of Supercomputing 2022 1 20 Search in Google Scholar

2. Alymani M, Alsolai H, Maashi M, Alhebri A, Alshahrani H, Al-Wesabi FN, Mohamed A, Hamza MA. Dispersal foraging strategy with cuckoo search optimization based path planning in unmanned aerial vehicle networks. IEEE Access 11. 2023; 31365–31372. AlymaniM AlsolaiH MaashiM AlhebriA AlshahraniH Al-WesabiFN MohamedA HamzaMA Dispersal foraging strategy with cuckoo search optimization based path planning in unmanned aerial vehicle network IEEE Access 11 2023 31365 31372 Search in Google Scholar

3. Bozek P, Karavaev YL, Ardentov AA, Yefremov KS. Neural network control of a wheeled mobile robot based on optimal trajectories. International journal of advanced robotic systems 2020;17: 2. BozekP KaravaevYL ArdentovAA YefremovKS Neural network control of a wheeled mobile robot based on optimal trajectories International journal of advanced robotic systems 2020 17 2 Search in Google Scholar

4. Cao X, Zuo F. A fuzzy-based potential field hierarchical reinforcement learning approach for target hunting by multi-auv in 3-d underwater environments. International Journal of Control 2021;94(5):1334–1343. CaoX ZuoF A fuzzy-based potential field hierarchical reinforcement learning approach for target hunting by multi-auv in 3-d underwater environments International Journal of Control 2021 94 5 1334 1343 Search in Google Scholar

5. Duan S, Wang Q, Han X. Improved a-star algorithm for safety insured optimal path with smoothed corner turns. Journal of Mechanical Engineering. 2020;56(18): 205–215. DuanS WangQ HanX Improved a-star algorithm for safety insured optimal path with smoothed corner turns Journal of Mechanical Engineering 2020 56 18 205 215 Search in Google Scholar

6. Fu W, Wang B, Li X, Liu L, Wang Y. Ascent trajectory optimization for hypersonic vehicle based on improved chicken swarm optimization. IEEE Access 7. 2019;151836–151850. FuW WangB LiX LiuL WangY Ascent trajectory optimization for hypersonic vehicle based on improved chicken swarm optimizatio IEEE Access 7 2019 151836 151850 Search in Google Scholar

7. Gosiewski Z, Kwaśniewski K. Time minimization of rescue action realized by an autonomous vehicle. Electronics 9. 2020;12: 2099. GosiewskiZ KwaśniewskiK Time minimization of rescue action realized by an autonomous vehicl Electronics 9 2020 12 2099 Search in Google Scholar

8. Halliday D, Resnick R, Walker J. Fundamentals of physics. John Wiley & Sons; 2013. Halliday D Resnick R Walker J Fundamentals of physics John Wiley & Sons 2013 Search in Google Scholar

9. Jiang A, Yao X, Zhou J. Research on path planning of real-time obstacle avoidance of mechanical arm based on genetic algorithm. The Journal of Engineering. 2018;16: 1579–1586. JiangA YaoX ZhouJ Research on path planning of real-time obstacle avoidance of mechanical arm based on genetic algorithm The Journal of Engineering 2018 16 1579 1586 Search in Google Scholar

10. Kwaśniewski KK, Gosiewski Z. Genetic algorithm for mobile robot route planning with obstacle avoidance. acta mechanica et automatica. 2018; 12(2): 151–159. KwaśniewskiKK GosiewskiZ Genetic algorithm for mobile robot route planning with obstacle avoidance acta mechanica et automatica 2018 12 2 151 159 Search in Google Scholar

11. Li G, Yamashita A, Asama H, Tamura Y. An efficient improved artificial potential field based regression search method for robot path planning. In 2012 IEEE International Conference on Mechatronics and Automation. 2012; 1227–1232. LiG YamashitaA AsamaH TamuraY An efficient improved artificial potential field based regression search method for robot path planning In 2012 IEEE International Conference on Mechatronics and Automation 2012 1227 1232 Search in Google Scholar

12. Li L, Shi D, Jin S, Yang S, Zhou C, Lian Y, Liu H. Exact and Heuristic Multi-Robot Dubins Coverage Path Planning for Known Environments. Sensors. 2023;23(5):2560. LiL ShiD JinS YangS ZhouC LianY LiuH Exact and Heuristic Multi-Robot Dubins Coverage Path Planning for Known Environments Sensors 2023 23 5 2560 Search in Google Scholar

13. Li L, Shi D, Jin S, Yang S, Lian Y, Liu H. SP2E: Online spiral coverage with proactive prevention extremum for unknown environments. Journal of Intelligent & Robotic Systems; 2023; 108(2): 30. LiL ShiD JinS YangS LianY LiuH SP2E: Online spiral coverage with proactive prevention extremum for unknown environments Journal of Intelligent & Robotic Systems; 2023 108 2 30 Search in Google Scholar

14. Lo CC, Yu SW. A two-phased evolutionary approach for intelligent task assignment & scheduling. In 2015 11th international conference on natural computation (ICNC). 2015; 1092–1097. LoCC YuSW A two-phased evolutionary approach for intelligent task assignment & schedulin In 2015 11th international conference on natural computation (ICNC) 2015 1092 1097 Search in Google Scholar

15. Norhafezah K, Nurfadzliana A, Megawati O. Simulation of municipal solid waste route optimization by dijkstra’s algorithm. Journal of Fundamental and Applied Sciences 9. 2017; 732–747. NorhafezahK NurfadzlianaA MegawatiO Simulation of municipal solid waste route optimization by dijkstra’s algorith Journal of Fundamental and Applied Sciences 9 2017 732 747 Search in Google Scholar

16. Pawlowski A, Romaniuk S, Kulesza Z, Petrović M. Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm. IAES International Journal of Robotics and Automation (IJRA). 2022; 11(4): 263–277. PawlowskiA RomaniukS KuleszaZ PetrovićM Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm IAES International Journal of Robotics and Automation (IJRA) 2022 11 4 263 277 Search in Google Scholar

17. Petrović M, Miljković Z, Jokić A. A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm. Applied Soft Computing 81. 2019;105520. PetrovićM MiljkovićZ JokićA A novel methodology for optimal single mobile robot scheduling using whale optimization algorith Applied Soft Computing 81 2019 105520 Search in Google Scholar

18. Piegl L,Tiller W. The NURBS book. Springer Science & Business Media; 1996. Piegl L Tiller W The NURBS book Springer Science & Business Media 1996 Search in Google Scholar

19. Salt L, Howard D, Indiveri G, Sandamirskaya Y. Parameter optimization and learning in a spiking neural network for uav obstacle avoidance targeting neuromorphic processors. IEEE transactions on neural networks and learning systems. 2019; 31(9): 3305–3318. SaltL HowardD IndiveriG SandamirskayaY Parameter optimization and learning in a spiking neural network for uav obstacle avoidance targeting neuromorphic processors IEEE transactions on neural networks and learning systems 2019 31 9 3305 3318 Search in Google Scholar

20. Shi J, Liu C, Xi H. Improved d* path planning algorithm based on CA model. Journal of Electronic Measurement & Instrumentation; 2016. Shi J Liu C Xi H Improved d* path planning algorithm based on CA model Journal of Electronic Measurement & Instrumentation 2016 Search in Google Scholar

21. Singla A, Padakandla S, Bhatnagar S. Memory-based deep reinforcement learning for obstacle avoidance in uav with limited environment knowledge. IEEE Transactions on Intelligent Transportation Systems. 2019; 22(1): 107–118. SinglaA PadakandlaS BhatnagarS Memory-based deep reinforcement learning for obstacle avoidance in uav with limited environment knowledge IEEE Transactions on Intelligent Transportation Systems 2019 22 1 107 118 Search in Google Scholar

22. Wang Z, Zeng G, Huang B, Fang Z. Global optimal path planning for robots with improved A* algorithm. Journal of Computer Applications. 2019;39(9): 2517. WangZ ZengG HuangB FangZ Global optimal path planning for robots with improved A* algorithm Journal of Computer Applications 2019 39 9 2517 Search in Google Scholar

23. Yi Z, Yanan Z, Xiangde L. Path planning of multiple industrial mobile robots based on ant colony algorithm. In 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing. 2019; 406–409. YiZ YananZ XiangdeL Path planning of multiple industrial mobile robots based on ant colony algorith In 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing 2019 406 409 Search in Google Scholar

24. Zhang T, Xu J, Wu B. Hybrid path planning model for multiple robots considering obstacle avoidance. IEEE Access 10. 2022;71914–71935. ZhangT XuJ WuB Hybrid path planning model for multiple robots considering obstacle avoidanc IEEE Access 10 2022 71914 71935 Search in Google Scholar

25. Zhu H, Ouyang H, Xi H. Neural network-based time optimal trajectory planning method for rotary cranes with obstacle avoidance. Mechanical Systems and Signal Processing 185. 2023;109777. ZhuH OuyangH XiH Neural network-based time optimal trajectory planning method for rotary cranes with obstacle avoidanc Mechanical Systems and Signal Processing 185 2023 109777 Search in Google Scholar